class_docstr: Masked language model network head for BERT modeling.
  
  This network implements a masked language model based on the provided network.
  It assumes that the network being passed has a "get_embedding_table()" method.
  
  Attributes:
    input_width: The innermost dimension of the input tensor to this network.
    num_predictions: The number of predictions to make per sequence.
    source_network: The network with the embedding layer to use for the
      embedding layer.
    activation: The activation, if any, for the dense layer in this network.
    initializer: The intializer for the dense layer in this network. Defaults to
      a Glorot uniform initializer.
    output: The output style for this network. Can be either 'logits' or
      'predictions'.
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code: def get_config(self):
      raise NotImplementedError(
          "MaskedLM cannot be directly serialized at this "
          "time. Please use it only in Layers or "
          "functionally subclassed Models/Networks."
      )
  
file_path: autodist/examples/benchmark/utils/modeling/networks/masked_lm.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow.python.keras.engine import network
  from utils import tf_utils
label_desc: Use image node_id 2 for calling the MaskedLM obj's underlying member method code with example usage: obj.get_config() without return types